UofWashington:MachineLearning:House Prices:: price regression:: Houses_feat_set1
bdanalytics
Date: (Mon) Jan 04, 2016
Introduction:
Data: Washington Sate:Kings County:Seattle House Prices Source: Training: home_data.csv
New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<pointer>"; sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(name = "home_data.csv")
glbObsNewFile <- #list(url = "<obsNewFileName>") # default OR
#list(splitSpecs = list(method = NULL #select from c(NULL, "condition", "sample", "copy")
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# )
# )
list(splitSpecs = list(method = "sample", nRatio = 0.2, seed = 0))
glbInpMerge <- NULL #: default
# list(fnames = c("<fname1>", "<fname2>")) # files will be concatenated
glb_is_separate_newobs_dataset <- FALSE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # FALSE not supported - use "copy" for glbObsNewFile$splitSpecs$method # select from c(FALSE, TRUE)
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- NULL # or TRUE or FALSE
glb_rsp_var_raw <- "price"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "price.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL
# function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
# }
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, names(table(glbObsAll[, glb_rsp_var_raw]))))
glb_map_rsp_var_to_raw <- NULL
# function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
# }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "id.date" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "sqft.living.cut.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category & work each one in
,"id","date","waterfront","view"
,"condition","grade","sqft_above","sqft_basement","yr_built","yr_renovated","lat","long"
,"sqft_living15","sqft_lot15"
,".pos","sqft.living.cut.fctr"
#,"sqft_living","bedrooms","bathrooms","sqft_lot","floors","zipcode"
)
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glb_drop_vars <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
# character
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
glbFeatsDerive[["id.date"]] <- list(
mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
, args = c("id", "date"))
glbFeatsDerive[["sqft.living.cut.fctr"]] <- list(
mapfn = function(sqft_living) { return(cut(sqft_living, breaks = c(0, 1427, 1910, 2550, 14000))) }
,args = c("sqft_living"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S", timezone = "America/New_York", impute.na = TRUE,
# last.ctg = TRUE, poly.ctg = TRUE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["<mdlId>"]] <- FALSE
glbMdlAllowParallel[["Max.cor.Y##rcv#rpart"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c(glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. glbFeatsId, glbFeatsCategory & glb_rsp_var
)
# Output specs
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
glb_out_vars_lst <- list()
# glbFeatsId will be the first output column, by default
if (glb_is_classification && glb_is_binomial) {
glb_out_vars_lst[["Probability1"]] <-
"%<d-% mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob"
} else {
glb_out_vars_lst[[glb_rsp_var]] <-
"%<d-% mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value"
}
# glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glb_out_vars_lst[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glb_out_pfx <- "Houses_feat_set1_"
glb_save_envir <- FALSE # or TRUE
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 8.722 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/home_data.csv..."
## [1] "dimensions of data in ./data/home_data.csv: 21,613 rows x 21 cols"
## id date price bedrooms bathrooms sqft_living
## 1 7129300520 20141013T000000 221900 3 1.00 1180
## 2 6414100192 20141209T000000 538000 3 2.25 2570
## 3 5631500400 20150225T000000 180000 2 1.00 770
## 4 2487200875 20141209T000000 604000 4 3.00 1960
## 5 1954400510 20150218T000000 510000 3 2.00 1680
## 6 7237550310 20140512T000000 1225000 4 4.50 5420
## sqft_lot floors waterfront view condition grade sqft_above sqft_basement
## 1 5650 1 0 0 3 7 1180 0
## 2 7242 2 0 0 3 7 2170 400
## 3 10000 1 0 0 3 6 770 0
## 4 5000 1 0 0 5 7 1050 910
## 5 8080 1 0 0 3 8 1680 0
## 6 101930 1 0 0 3 11 3890 1530
## yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
## 1 1955 0 98178 47.5112 -122.257 1340 5650
## 2 1951 1991 98125 47.7210 -122.319 1690 7639
## 3 1933 0 98028 47.7379 -122.233 2720 8062
## 4 1965 0 98136 47.5208 -122.393 1360 5000
## 5 1987 0 98074 47.6168 -122.045 1800 7503
## 6 2001 0 98053 47.6561 -122.005 4760 101930
## id date price bedrooms bathrooms sqft_living
## 747 9277200111 20140714T000000 650000 4 1.75 2010
## 3293 7701961030 20150129T000000 875000 4 2.50 3600
## 11006 8651480550 20140623T000000 600000 3 2.50 2260
## 15728 6116500290 20140714T000000 799950 6 2.75 3040
## 15897 2131701410 20150427T000000 299950 3 2.25 1370
## 21390 3448740360 20150429T000000 418500 4 2.50 2190
## sqft_lot floors waterfront view condition grade sqft_above
## 747 5070 1 0 1 4 7 1300
## 3293 21794 2 0 0 4 11 3600
## 11006 10153 2 0 0 3 10 2260
## 15728 36721 1 0 3 4 9 1760
## 15897 5000 2 0 0 3 7 1370
## 21390 4866 2 0 0 3 7 2190
## sqft_basement yr_built yr_renovated zipcode lat long
## 747 710 1963 0 98116 47.5793 -122.402
## 3293 0 1990 0 98077 47.7121 -122.072
## 11006 0 1987 0 98074 47.6410 -122.068
## 15728 1280 1958 0 98166 47.4488 -122.356
## 15897 0 1990 0 98019 47.7372 -121.981
## 21390 0 2009 0 98059 47.4907 -122.152
## sqft_living15 sqft_lot15
## 747 2180 5400
## 3293 3410 19864
## 11006 2740 10153
## 15728 2420 21075
## 15897 1600 7724
## 21390 2190 5670
## id date price bedrooms bathrooms sqft_living
## 21608 2997800021 20150219T000000 475000 3 2.50 1310
## 21609 263000018 20140521T000000 360000 3 2.50 1530
## 21610 6600060120 20150223T000000 400000 4 2.50 2310
## 21611 1523300141 20140623T000000 402101 2 0.75 1020
## 21612 291310100 20150116T000000 400000 3 2.50 1600
## 21613 1523300157 20141015T000000 325000 2 0.75 1020
## sqft_lot floors waterfront view condition grade sqft_above
## 21608 1294 2 0 0 3 8 1180
## 21609 1131 3 0 0 3 8 1530
## 21610 5813 2 0 0 3 8 2310
## 21611 1350 2 0 0 3 7 1020
## 21612 2388 2 0 0 3 8 1600
## 21613 1076 2 0 0 3 7 1020
## sqft_basement yr_built yr_renovated zipcode lat long
## 21608 130 2008 0 98116 47.5773 -122.409
## 21609 0 2009 0 98103 47.6993 -122.346
## 21610 0 2014 0 98146 47.5107 -122.362
## 21611 0 2009 0 98144 47.5944 -122.299
## 21612 0 2004 0 98027 47.5345 -122.069
## 21613 0 2008 0 98144 47.5941 -122.299
## sqft_living15 sqft_lot15
## 21608 1330 1265
## 21609 1530 1509
## 21610 1830 7200
## 21611 1020 2007
## 21612 1410 1287
## 21613 1020 1357
## 'data.frame': 21613 obs. of 20 variables:
## $ id : num 7.13e+09 6.41e+09 5.63e+09 2.49e+09 1.95e+09 ...
## $ date : chr "20141013T000000" "20141209T000000" "20150225T000000" "20141209T000000" ...
## $ price : int 221900 538000 180000 604000 510000 1225000 257500 291850 229500 323000 ...
## $ bedrooms : int 3 3 2 4 3 4 3 3 3 3 ...
## $ bathrooms : num 1 2.25 1 3 2 4.5 2.25 1.5 1 2.5 ...
## $ sqft_living : int 1180 2570 770 1960 1680 5420 1715 1060 1780 1890 ...
## $ sqft_lot : int 5650 7242 10000 5000 8080 101930 6819 9711 7470 6560 ...
## $ floors : num 1 2 1 1 1 1 2 1 1 2 ...
## $ waterfront : int 0 0 0 0 0 0 0 0 0 0 ...
## $ view : int 0 0 0 0 0 0 0 0 0 0 ...
## $ condition : int 3 3 3 5 3 3 3 3 3 3 ...
## $ grade : int 7 7 6 7 8 11 7 7 7 7 ...
## $ sqft_above : int 1180 2170 770 1050 1680 3890 1715 1060 1050 1890 ...
## $ sqft_basement: int 0 400 0 910 0 1530 0 0 730 0 ...
## $ yr_built : int 1955 1951 1933 1965 1987 2001 1995 1963 1960 2003 ...
## $ yr_renovated : int 0 1991 0 0 0 0 0 0 0 0 ...
## $ zipcode : int 98178 98125 98028 98136 98074 98053 98003 98198 98146 98038 ...
## $ lat : num 47.5 47.7 47.7 47.5 47.6 ...
## $ long : num -122 -122 -122 -122 -122 ...
## $ sqft_living15: int 1340 1690 2720 1360 1800 4760 2238 1650 1780 2390 ...
## NULL
## 'data.frame': 21613 obs. of 21 variables:
## $ id : num 7.13e+09 6.41e+09 5.63e+09 2.49e+09 1.95e+09 ...
## $ date : chr "20141013T000000" "20141209T000000" "20150225T000000" "20141209T000000" ...
## $ price : int 221900 538000 180000 604000 510000 1225000 257500 291850 229500 323000 ...
## $ bedrooms : int 3 3 2 4 3 4 3 3 3 3 ...
## $ bathrooms : num 1 2.25 1 3 2 4.5 2.25 1.5 1 2.5 ...
## $ sqft_living : int 1180 2570 770 1960 1680 5420 1715 1060 1780 1890 ...
## $ sqft_lot : int 5650 7242 10000 5000 8080 101930 6819 9711 7470 6560 ...
## $ floors : num 1 2 1 1 1 1 2 1 1 2 ...
## $ waterfront : int 0 0 0 0 0 0 0 0 0 0 ...
## $ view : int 0 0 0 0 0 0 0 0 0 0 ...
## $ condition : int 3 3 3 5 3 3 3 3 3 3 ...
## $ grade : int 7 7 6 7 8 11 7 7 7 7 ...
## $ sqft_above : int 1180 2170 770 1050 1680 3890 1715 1060 1050 1890 ...
## $ sqft_basement: int 0 400 0 910 0 1530 0 0 730 0 ...
## $ yr_built : int 1955 1951 1933 1965 1987 2001 1995 1963 1960 2003 ...
## $ yr_renovated : int 0 1991 0 0 0 0 0 0 0 0 ...
## $ zipcode : int 98178 98125 98028 98136 98074 98053 98003 98198 98146 98038 ...
## $ lat : num 47.5 47.7 47.7 47.5 47.6 ...
## $ long : num -122 -122 -122 -122 -122 ...
## $ sqft_living15: int 1340 1690 2720 1360 1800 4760 2238 1650 1780 2390 ...
## $ sqft_lot15 : int 5650 7639 8062 5000 7503 101930 6819 9711 8113 7570 ...
## NULL## Warning in myprint_str_df(df): [list output truncated]## Loading required package: caTools## id date price bedrooms bathrooms sqft_living
## 4 2487200875 20141209T000000 604000 4 3.0 1960
## 17 1875500060 20140731T000000 395000 3 2.0 1890
## 23 7137970340 20140703T000000 285000 5 2.5 2270
## 28 3303700376 20141201T000000 667000 3 1.0 1400
## 30 1873100390 20150302T000000 719000 4 2.5 2570
## 32 2426039314 20141201T000000 280000 2 1.5 1190
## sqft_lot floors waterfront view condition grade sqft_above
## 4 5000 1.0 0 0 5 7 1050
## 17 14040 2.0 0 0 3 7 1890
## 23 6300 2.0 0 0 3 8 2270
## 28 1581 1.5 0 0 5 8 1400
## 30 7173 2.0 0 0 3 8 2570
## 32 1265 3.0 0 0 3 7 1190
## sqft_basement yr_built yr_renovated zipcode lat long
## 4 910 1965 0 98136 47.5208 -122.393
## 17 0 1994 0 98019 47.7277 -121.962
## 23 0 1995 0 98092 47.3266 -122.169
## 28 0 1909 0 98112 47.6221 -122.314
## 30 0 2005 0 98052 47.7073 -122.110
## 32 0 2005 0 98133 47.7274 -122.357
## sqft_living15 sqft_lot15
## 4 1360 5000
## 17 1890 14018
## 23 2240 7005
## 28 1860 3861
## 30 2630 6026
## 32 1390 1756
## id date price bedrooms bathrooms sqft_living
## 6707 9109000050 20140709T000000 275000 3 1.00 1200
## 8429 263000329 20141008T000000 349950 3 2.50 1420
## 12423 2172000570 20140624T000000 317000 5 2.50 2360
## 12802 7701700040 20140925T000000 320000 3 1.75 1510
## 13577 9269200150 20140715T000000 390000 1 1.75 1440
## 17032 2742100009 20140506T000000 385000 3 1.75 1900
## sqft_lot floors waterfront view condition grade sqft_above
## 6707 7800 1.0 0 0 4 7 1200
## 8429 1162 3.0 0 0 3 8 1420
## 12423 11375 1.0 0 0 4 7 1180
## 12802 30185 1.5 0 0 3 7 1510
## 13577 4920 1.0 0 0 3 7 720
## 17032 5520 1.0 0 0 3 7 1280
## sqft_basement yr_built yr_renovated zipcode lat long
## 6707 0 1954 0 98126 47.5196 -122.371
## 8429 0 2002 0 98103 47.6982 -122.349
## 12423 1180 1962 0 98178 47.4875 -122.255
## 12802 0 1976 0 98058 47.4118 -122.089
## 13577 720 1923 0 98126 47.5340 -122.376
## 17032 620 1982 0 98118 47.5549 -122.292
## sqft_living15 sqft_lot15
## 6707 1230 7070
## 8429 1430 1560
## 12423 1160 7800
## 12802 1470 12465
## 13577 1440 4920
## 17032 1330 5196
## id date price bedrooms bathrooms sqft_living
## 21593 1931300412 20150416T000000 475000 3 2.25 1190
## 21597 7502800100 20140813T000000 679950 5 2.75 3600
## 21602 5100403806 20150407T000000 467000 3 2.50 1425
## 21603 844000965 20140626T000000 224000 3 1.75 1500
## 21610 6600060120 20150223T000000 400000 4 2.50 2310
## 21613 1523300157 20141015T000000 325000 2 0.75 1020
## sqft_lot floors waterfront view condition grade sqft_above
## 21593 1200 3 0 0 3 8 1190
## 21597 9437 2 0 0 3 9 3600
## 21602 1179 3 0 0 3 8 1425
## 21603 11968 1 0 0 3 6 1500
## 21610 5813 2 0 0 3 8 2310
## 21613 1076 2 0 0 3 7 1020
## sqft_basement yr_built yr_renovated zipcode lat long
## 21593 0 2008 0 98103 47.6542 -122.346
## 21597 0 2014 0 98059 47.4822 -122.131
## 21602 0 2008 0 98125 47.6963 -122.318
## 21603 0 2014 0 98010 47.3095 -122.002
## 21610 0 2014 0 98146 47.5107 -122.362
## 21613 0 2008 0 98144 47.5941 -122.299
## sqft_living15 sqft_lot15
## 21593 1180 1224
## 21597 3550 9421
## 21602 1285 1253
## 21603 1320 11303
## 21610 1830 7200
## 21613 1020 1357
## 'data.frame': 3772 obs. of 21 variables:
## $ id : num 2.49e+09 1.88e+09 7.14e+09 3.30e+09 1.87e+09 ...
## $ date : chr "20141209T000000" "20140731T000000" "20140703T000000" "20141201T000000" ...
## $ price : int 604000 395000 285000 667000 719000 280000 640000 785000 345000 600000 ...
## $ bedrooms : int 4 3 5 3 4 2 4 4 5 3 ...
## $ bathrooms : num 3 2 2.5 1 2.5 1.5 2 2.5 2.5 1.75 ...
## $ sqft_living : int 1960 1890 2270 1400 2570 1190 2360 2290 3150 1410 ...
## $ sqft_lot : int 5000 14040 6300 1581 7173 1265 6000 13416 9134 4080 ...
## $ floors : num 1 2 2 1.5 2 3 2 2 1 1 ...
## $ waterfront : int 0 0 0 0 0 0 0 0 0 0 ...
## $ view : int 0 0 0 0 0 0 0 0 0 0 ...
## $ condition : int 5 3 3 5 3 3 4 4 4 4 ...
## $ grade : int 7 7 8 8 8 7 8 9 8 7 ...
## $ sqft_above : int 1050 1890 2270 1400 2570 1190 2360 2290 1640 1000 ...
## $ sqft_basement: int 910 0 0 0 0 0 0 0 1510 410 ...
## $ yr_built : int 1965 1994 1995 1909 2005 2005 1904 1981 1966 1950 ...
## $ yr_renovated : int 0 0 0 0 0 0 0 0 0 0 ...
## $ zipcode : int 98136 98019 98092 98112 98052 98133 98107 98007 98056 98117 ...
## $ lat : num 47.5 47.7 47.3 47.6 47.7 ...
## $ long : num -122 -122 -122 -122 -122 ...
## $ sqft_living15: int 1360 1890 2240 1860 2630 1390 1730 2680 1990 1410 ...
## $ sqft_lot15 : int 5000 14018 7005 3861 6026 1756 4700 13685 9133 4080 ...
## - attr(*, "comment")= chr "glbObsNew"
## id date price bedrooms bathrooms sqft_living
## 1 7129300520 20141013T000000 221900 3 1.00 1180
## 2 6414100192 20141209T000000 538000 3 2.25 2570
## 3 5631500400 20150225T000000 180000 2 1.00 770
## 5 1954400510 20150218T000000 510000 3 2.00 1680
## 6 7237550310 20140512T000000 1225000 4 4.50 5420
## 7 1321400060 20140627T000000 257500 3 2.25 1715
## sqft_lot floors waterfront view condition grade sqft_above sqft_basement
## 1 5650 1 0 0 3 7 1180 0
## 2 7242 2 0 0 3 7 2170 400
## 3 10000 1 0 0 3 6 770 0
## 5 8080 1 0 0 3 8 1680 0
## 6 101930 1 0 0 3 11 3890 1530
## 7 6819 2 0 0 3 7 1715 0
## yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
## 1 1955 0 98178 47.5112 -122.257 1340 5650
## 2 1951 1991 98125 47.7210 -122.319 1690 7639
## 3 1933 0 98028 47.7379 -122.233 2720 8062
## 5 1987 0 98074 47.6168 -122.045 1800 7503
## 6 2001 0 98053 47.6561 -122.005 4760 101930
## 7 1995 0 98003 47.3097 -122.327 2238 6819
## id date price bedrooms bathrooms sqft_living
## 813 4307350200 20150512T000000 347000 3 2.5 1680
## 1260 2798600240 20141114T000000 295700 4 2.5 1720
## 4438 3874900090 20150326T000000 448000 2 2.0 1670
## 10258 1995200200 20140506T000000 313950 3 1.0 1510
## 12776 7812800155 20150318T000000 170000 3 1.0 790
## 17617 4036800900 20140924T000000 447000 3 1.0 1310
## sqft_lot floors waterfront view condition grade sqft_above
## 813 4308 2 0 0 3 7 1680
## 1260 5805 2 0 0 3 8 1720
## 4438 7772 1 0 0 4 6 860
## 10258 6083 1 0 0 4 6 860
## 12776 6750 1 0 0 2 6 790
## 17617 7000 1 0 0 4 7 1310
## sqft_basement yr_built yr_renovated zipcode lat long
## 813 0 2004 0 98056 47.4802 -122.179
## 1260 0 2000 0 98092 47.3286 -122.208
## 4438 810 1919 0 98126 47.5461 -122.377
## 10258 650 1940 0 98115 47.6966 -122.324
## 12776 0 1944 0 98178 47.4984 -122.240
## 17617 0 1958 0 98008 47.6019 -122.123
## sqft_living15 sqft_lot15
## 813 2160 4182
## 1260 2360 7700
## 4438 1300 7770
## 10258 1510 5712
## 12776 960 6298
## 17617 1280 7300
## id date price bedrooms bathrooms sqft_living
## 21606 3448900210 20141014T000000 610685 4 2.50 2520
## 21607 7936000429 20150326T000000 1007500 4 3.50 3510
## 21608 2997800021 20150219T000000 475000 3 2.50 1310
## 21609 263000018 20140521T000000 360000 3 2.50 1530
## 21611 1523300141 20140623T000000 402101 2 0.75 1020
## 21612 291310100 20150116T000000 400000 3 2.50 1600
## sqft_lot floors waterfront view condition grade sqft_above
## 21606 6023 2 0 0 3 9 2520
## 21607 7200 2 0 0 3 9 2600
## 21608 1294 2 0 0 3 8 1180
## 21609 1131 3 0 0 3 8 1530
## 21611 1350 2 0 0 3 7 1020
## 21612 2388 2 0 0 3 8 1600
## sqft_basement yr_built yr_renovated zipcode lat long
## 21606 0 2014 0 98056 47.5137 -122.167
## 21607 910 2009 0 98136 47.5537 -122.398
## 21608 130 2008 0 98116 47.5773 -122.409
## 21609 0 2009 0 98103 47.6993 -122.346
## 21611 0 2009 0 98144 47.5944 -122.299
## 21612 0 2004 0 98027 47.5345 -122.069
## sqft_living15 sqft_lot15
## 21606 2520 6023
## 21607 2050 6200
## 21608 1330 1265
## 21609 1530 1509
## 21611 1020 2007
## 21612 1410 1287
## 'data.frame': 17841 obs. of 21 variables:
## $ id : num 7.13e+09 6.41e+09 5.63e+09 1.95e+09 7.24e+09 ...
## $ date : chr "20141013T000000" "20141209T000000" "20150225T000000" "20150218T000000" ...
## $ price : int 221900 538000 180000 510000 1225000 257500 291850 229500 323000 662500 ...
## $ bedrooms : int 3 3 2 3 4 3 3 3 3 3 ...
## $ bathrooms : num 1 2.25 1 2 4.5 2.25 1.5 1 2.5 2.5 ...
## $ sqft_living : int 1180 2570 770 1680 5420 1715 1060 1780 1890 3560 ...
## $ sqft_lot : int 5650 7242 10000 8080 101930 6819 9711 7470 6560 9796 ...
## $ floors : num 1 2 1 1 1 2 1 1 2 1 ...
## $ waterfront : int 0 0 0 0 0 0 0 0 0 0 ...
## $ view : int 0 0 0 0 0 0 0 0 0 0 ...
## $ condition : int 3 3 3 3 3 3 3 3 3 3 ...
## $ grade : int 7 7 6 8 11 7 7 7 7 8 ...
## $ sqft_above : int 1180 2170 770 1680 3890 1715 1060 1050 1890 1860 ...
## $ sqft_basement: int 0 400 0 0 1530 0 0 730 0 1700 ...
## $ yr_built : int 1955 1951 1933 1987 2001 1995 1963 1960 2003 1965 ...
## $ yr_renovated : int 0 1991 0 0 0 0 0 0 0 0 ...
## $ zipcode : int 98178 98125 98028 98074 98053 98003 98198 98146 98038 98007 ...
## $ lat : num 47.5 47.7 47.7 47.6 47.7 ...
## $ long : num -122 -122 -122 -122 -122 ...
## $ sqft_living15: int 1340 1690 2720 1800 4760 2238 1650 1780 2390 2210 ...
## $ sqft_lot15 : int 5650 7639 8062 7503 101930 6819 9711 8113 7570 8925 ...
## - attr(*, "comment")= chr "glbObsTrn"## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: id.date..."
## [1] "Creating new feature: sqft.living.cut.fctr..."## [1] "Partition stats:"## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## price.cut.fctr .src .n
## 1 (6.74e+04,2.62e+06] Train 17767
## 2 (6.74e+04,2.62e+06] Test 3764
## 3 (2.62e+06,5.16e+06] Train 68
## 4 (2.62e+06,5.16e+06] Test 8
## 5 (5.16e+06,7.71e+06] Train 6
## price.cut.fctr .src .n
## 1 (6.74e+04,2.62e+06] Train 17767
## 2 (6.74e+04,2.62e+06] Test 3764
## 3 (2.62e+06,5.16e+06] Train 68
## 4 (2.62e+06,5.16e+06] Test 8
## 5 (5.16e+06,7.71e+06] Train 6
## .src .n
## 1 Train 17841
## 2 Test 3772## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 8.722 16.182 7.46
## 2 inspect.data 2 0 0 16.182 NA NAStep 2.0: inspect data
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## bedrooms bathrooms waterfront view sqft_basement
## 13 10 21450 19489 13126
## yr_renovated
## 20699
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## date id.date
## 0 0
## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.
## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 16.182 32.09 15.908
## 3 scrub.data 2 1 1 32.091 NA NAStep 2.1: scrub data
## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## bedrooms bathrooms waterfront view sqft_basement
## 13 10 21450 19489 13126
## yr_renovated
## 20699
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## date id.date
## 0 0## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 32.091 38.302 6.211
## 4 transform.data 2 2 2 38.302 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 38.302 38.342 0.04
## 5 extract.features 3 0 0 38.343 NA NAStep 3.0: extract features
## label step_major step_minor label_minor bgn end
## 5 extract.features 3 0 0 38.343 38.372
## 6 extract.features.string 3 1 1 38.373 NA
## elapsed
## 5 0.029
## 6 NAStep 3.1: extract features string
## label step_major step_minor label_minor bgn end
## 1 extract.features.string.bgn 1 0 0 38.403 NA
## elapsed
## 1 NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 38.403 38.413 0.01
## 2 0 38.413 NA NA## date .src id.date
## "date" ".src" "id.date"## label step_major step_minor label_minor bgn
## 6 extract.features.string 3 1 1 38.373
## 7 extract.features.datetime 3 2 2 38.427
## end elapsed
## 6 38.427 0.054
## 7 NA NAStep 3.2: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 38.453
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 7 extract.features.datetime 3 2 2 38.427
## 8 extract.features.price 3 3 3 38.463
## end elapsed
## 7 38.463 0.036
## 8 NA NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 38.488 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 38.463 38.498
## 9 extract.features.text 3 4 4 38.498 NA
## elapsed
## 8 0.035
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 38.542 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 9 extract.features.text 3 4 4 38.498 38.551
## 10 extract.features.end 3 5 5 38.552 NA
## elapsed
## 9 0.053
## 10 NAStep 3.5: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 10 extract.features.end 3 5 5 38.552 39.428
## 11 manage.missing.data 4 0 0 39.429 NA
## elapsed
## 10 0.876
## 11 NAStep 4.0: manage missing data
## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## bedrooms bathrooms waterfront view sqft_basement
## 13 10 21450 19489 13126
## yr_renovated
## 20699
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## date id.date
## 0 0## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## bedrooms bathrooms waterfront view sqft_basement
## 13 10 21450 19489 13126
## yr_renovated
## 20699
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## date id.date
## 0 0## label step_major step_minor label_minor bgn end
## 11 manage.missing.data 4 0 0 39.429 39.74
## 12 cluster.data 5 0 0 39.741 NA
## elapsed
## 11 0.312
## 12 NAStep 5.0: cluster data
## label step_major step_minor label_minor bgn end
## 12 cluster.data 5 0 0 39.741 39.802
## 13 partition.data.training 6 0 0 39.803 NA
## elapsed
## 12 0.061
## 13 NAStep 6.0: partition data training
## [1] "Newdata contains non-NA data for price; setting OOB to Newdata"## sqft.living.cut.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 2 (1.43e+03,1.91e+03] 4428 988 988 0.2481924 0.2619300
## 3 (1.91e+03,2.55e+03] 4471 963 963 0.2506025 0.2553022
## 1 (0,1.43e+03] 4455 949 949 0.2497057 0.2515907
## 4 (2.55e+03,1.4e+04] 4487 872 872 0.2514994 0.2311771
## .freqRatio.Tst
## 2 0.2619300
## 3 0.2553022
## 1 0.2515907
## 4 0.2311771## [1] "glbObsAll: "## [1] 21613 27## [1] "glbObsTrn: "## [1] 17841 27## [1] "glbObsFit: "## [1] 17841 26## [1] "glbObsOOB: "## [1] 3772 26## [1] "glbObsNew: "## [1] 3772 26## label step_major step_minor label_minor bgn end
## 13 partition.data.training 6 0 0 39.803 40.36
## 14 select.features 7 0 0 40.360 NA
## elapsed
## 13 0.557
## 14 NAStep 7.0: select features
## Loading required package: reshape2## [1] "cor(bathrooms, sqft_living)=0.7594"
## [1] "cor(price, bathrooms)=0.5301"
## [1] "cor(price, sqft_living)=0.7084"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified bathrooms as highly correlated with sqft_living## cor.y exclude.as.feat cor.y.abs cor.high.X
## sqft_living 0.708441487 0 0.708441487 <NA>
## grade 0.668890914 1 0.668890914 <NA>
## sqft_above 0.610988429 1 0.610988429 <NA>
## sqft_living15 0.586792062 1 0.586792062 <NA>
## bathrooms 0.530073438 0 0.530073438 sqft_living
## sqft.living.cut.fctr 0.523122591 1 0.523122591 <NA>
## view 0.406939514 1 0.406939514 <NA>
## sqft_basement 0.331786255 1 0.331786255 <NA>
## bedrooms 0.310672803 0 0.310672803 <NA>
## lat 0.302852840 1 0.302852840 <NA>
## waterfront 0.278224722 1 0.278224722 <NA>
## floors 0.256425528 0 0.256425528 <NA>
## yr_renovated 0.122721006 1 0.122721006 <NA>
## sqft_lot 0.090440967 0 0.090440967 <NA>
## sqft_lot15 0.084523271 1 0.084523271 <NA>
## yr_built 0.056360729 1 0.056360729 <NA>
## condition 0.036979576 1 0.036979576 <NA>
## .pos 0.028282285 1 0.028282285 <NA>
## long 0.022417163 1 0.022417163 <NA>
## .rnorm -0.007611976 0 0.007611976 <NA>
## id -0.021573175 1 0.021573175 <NA>
## zipcode -0.053505492 0 0.053505492 <NA>
## freqRatio percentUnique zeroVar nzv
## sqft_living 1.035398 5.40328457 FALSE FALSE
## grade 1.463147 0.06726080 FALSE FALSE
## sqft_above 1.017143 4.89882854 FALSE FALSE
## sqft_living15 1.081761 4.05806849 FALSE FALSE
## bathrooms 1.395971 0.16815201 FALSE FALSE
## sqft.living.cut.fctr 1.003579 0.02242027 FALSE FALSE
## view 20.557545 0.02802533 FALSE TRUE
## sqft_basement 59.697802 1.64788969 FALSE TRUE
## bedrooms 1.432047 0.07286587 FALSE FALSE
## lat 1.066667 27.26865086 FALSE FALSE
## waterfront 126.435714 0.01121013 FALSE TRUE
## floors 1.286028 0.03363040 FALSE FALSE
## yr_renovated 224.842105 0.39235469 FALSE TRUE
## sqft_lot 1.219409 48.19797097 FALSE FALSE
## sqft_lot15 1.149007 43.34398296 FALSE FALSE
## yr_built 1.322835 0.65018777 FALSE FALSE
## condition 2.480522 0.02802533 FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE
## long 1.032609 4.09169890 FALSE FALSE
## .rnorm 1.000000 100.00000000 FALSE FALSE
## id 1.000000 99.34981223 FALSE FALSE
## zipcode 1.004090 0.39235469 FALSE FALSE
## is.cor.y.abs.low
## sqft_living FALSE
## grade FALSE
## sqft_above FALSE
## sqft_living15 FALSE
## bathrooms FALSE
## sqft.living.cut.fctr FALSE
## view FALSE
## sqft_basement FALSE
## bedrooms FALSE
## lat FALSE
## waterfront FALSE
## floors FALSE
## yr_renovated FALSE
## sqft_lot FALSE
## sqft_lot15 FALSE
## yr_built FALSE
## condition FALSE
## .pos FALSE
## long FALSE
## .rnorm FALSE
## id FALSE
## zipcode FALSE## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## view 0.4069395 1 0.4069395 <NA> 20.55754
## sqft_basement 0.3317863 1 0.3317863 <NA> 59.69780
## waterfront 0.2782247 1 0.2782247 <NA> 126.43571
## yr_renovated 0.1227210 1 0.1227210 <NA> 224.84211
## percentUnique zeroVar nzv is.cor.y.abs.low
## view 0.02802533 FALSE TRUE FALSE
## sqft_basement 1.64788969 FALSE TRUE FALSE
## waterfront 0.01121013 FALSE TRUE FALSE
## yr_renovated 0.39235469 FALSE TRUE FALSE
## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## bedrooms bathrooms waterfront view sqft_basement
## 13 10 21450 19489 13126
## yr_renovated
## 20699
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## date id.date .lcn
## 0 0 0## [1] "glb_feats_df:"## [1] 22 12## id exclude.as.feat rsp_var
## price price TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## price price NA TRUE NA <NA> NA
## percentUnique zeroVar nzv is.cor.y.abs.low interaction.feat
## price NA NA NA NA NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## price NA NA NA TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 14 select.features 7 0 0 40.360 44.347 3.988
## 15 fit.models 8 0 0 44.348 NA NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 44.887 NA NA# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 1 fit.models_0_bgn 1 0 setup 44.887
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet 44.921
## end elapsed
## 1 44.92 0.033
## 2 NA NAret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indep_vars: sqft_living,bedrooms"## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 5360 on full training set
## Length Class Mode
## a0 81 -none- numeric
## beta 162 dgCMatrix S4
## df 81 -none- numeric
## dim 2 -none- numeric
## lambda 81 -none- numeric
## dev.ratio 81 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) bedrooms sqft_living
## 66087.5678 -51120.1213 311.8162
## [1] "max lambda < lambdaOpt:"
## (Intercept) bedrooms sqft_living
## 66343.4592 -51648.3600 312.5465
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet sqft_living,bedrooms 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.666 0.013 0.5151398
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 263425.4 0.5150854 0.4469051 229769.9 0.4466116if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indep_vars: sqft_living,bedrooms"## Loading required package: rpart## + Fold1.Rep1: cp=0.02189
## - Fold1.Rep1: cp=0.02189
## + Fold2.Rep1: cp=0.02189
## - Fold2.Rep1: cp=0.02189
## + Fold3.Rep1: cp=0.02189
## - Fold3.Rep1: cp=0.02189
## + Fold1.Rep2: cp=0.02189
## - Fold1.Rep2: cp=0.02189
## + Fold2.Rep2: cp=0.02189
## - Fold2.Rep2: cp=0.02189
## + Fold3.Rep2: cp=0.02189
## - Fold3.Rep2: cp=0.02189
## + Fold1.Rep3: cp=0.02189
## - Fold1.Rep3: cp=0.02189
## + Fold2.Rep3: cp=0.02189
## - Fold2.Rep3: cp=0.02189
## + Fold3.Rep3: cp=0.02189
## - Fold3.Rep3: cp=0.02189## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0219 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 17841
##
## CP nsplit rel error
## 1 0.31728367 0 1.0000000
## 2 0.08409584 1 0.6827163
## 3 0.06467334 2 0.5986205
## 4 0.03180436 3 0.5339472
## 5 0.02188796 4 0.5021428
##
## Variable importance
## sqft_living bedrooms
## 98 2
##
## Node number 1: 17841 observations, complexity param=0.3172837
## mean=545016.4, MSE=1.431195e+11
## left son=2 (16315 obs) right son=3 (1526 obs)
## Primary splits:
## sqft_living < 3395 to the left, improve=0.31728370, (0 missing)
## bedrooms < 3.5 to the left, improve=0.08843748, (0 missing)
## Surrogate splits:
## bedrooms < 7.5 to the left, agree=0.915, adj=0.005, (0 split)
##
## Node number 2: 16315 observations, complexity param=0.08409584
## mean=479845, MSE=5.724161e+10
## left son=4 (11976 obs) right son=5 (4339 obs)
## Primary splits:
## sqft_living < 2329.5 to the left, improve=0.22992900, (0 missing)
## bedrooms < 3.5 to the left, improve=0.05768213, (0 missing)
## Surrogate splits:
## bedrooms < 4.5 to the left, agree=0.756, adj=0.084, (0 split)
##
## Node number 3: 1526 observations, complexity param=0.06467334
## mean=1241787, MSE=5.303721e+11
## left son=6 (1471 obs) right son=7 (55 obs)
## Primary splits:
## sqft_living < 6180 to the left, improve=0.20403660, (0 missing)
## bedrooms < 4.5 to the left, improve=0.00701696, (0 missing)
##
## Node number 4: 11976 observations
## mean=410790.6, MSE=3.01359e+10
##
## Node number 5: 4339 observations
## mean=670441, MSE=8.256728e+10
##
## Node number 6: 1471 observations, complexity param=0.03180436
## mean=1178177, MSE=3.475049e+11
## left son=12 (1018 obs) right son=13 (453 obs)
## Primary splits:
## sqft_living < 4227.5 to the left, improve=0.158866100, (0 missing)
## bedrooms < 8.5 to the right, improve=0.001144926, (0 missing)
##
## Node number 7: 55 observations
## mean=2943042, MSE=2.418756e+12
##
## Node number 12: 1018 observations
## mean=1021441, MSE=1.968955e+11
##
## Node number 13: 453 observations
## mean=1530403, MSE=5.066908e+11
##
## n= 17841
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 17841 2.553395e+15 545016.4
## 2) sqft_living< 3395 16315 9.338969e+14 479845.0
## 4) sqft_living< 2329.5 11976 3.609075e+14 410790.6 *
## 5) sqft_living>=2329.5 4339 3.582594e+14 670441.0 *
## 3) sqft_living>=3395 1526 8.093479e+14 1241787.0
## 6) sqft_living< 6180 1471 5.111797e+14 1178177.0
## 12) sqft_living< 4227.5 1018 2.004396e+14 1021441.0 *
## 13) sqft_living>=4227.5 453 2.295310e+14 1530403.0 *
## 7) sqft_living>=6180 55 1.330316e+14 2943042.0 *
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart sqft_living,bedrooms 5
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 3.112 0.076 0.4978572
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 271576.7 NA 0.3939231 240523.3 NA
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.485892 4805.365 0.01258887if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## bgn end elapsed
## 2 44.921 51.831 6.91
## 3 51.832 NA NA
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indep_vars: sqft_living,bedrooms,sqft_living:sqft_living"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 1155 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 81 -none- numeric
## beta 162 dgCMatrix S4
## df 81 -none- numeric
## dim 2 -none- numeric
## lambda 81 -none- numeric
## dev.ratio 81 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) bedrooms sqft_living
## 68266.2274 -55537.4289 317.9052
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "bedrooms" "sqft_living"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats max.nTuningRuns
## 1 sqft_living,bedrooms,sqft_living:sqft_living 25
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 5.07 0.01 0.5153775
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 263555 0.5153231 0.445158 230132.5 0.4448636
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5152638 6967.464 0.003882658# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## 4 fit.models_0_Low.cor.X 1 3 glmnet
## bgn end elapsed
## 3 51.832 58.502 6.67
## 4 58.503 NA NAindep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indep_vars: sqft_living,bedrooms,floors,sqft_lot,.rnorm,zipcode"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 1155 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 66 -none- numeric
## beta 396 dgCMatrix S4
## df 66 -none- numeric
## dim 2 -none- numeric
## lambda 66 -none- numeric
## dev.ratio 66 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 6 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) bedrooms sqft_living sqft_lot zipcode
## -5.412106e+07 -5.375660e+04 3.254768e+02 -2.867278e-01 5.523352e+02
## [1] "max lambda < lambdaOpt:"
## (Intercept) .rnorm bedrooms sqft_living sqft_lot
## -5.432339e+07 -1.504634e+01 -5.403620e+04 3.258028e+02 -2.900121e-01
## zipcode
## 5.544013e+02
## id
## 1 Low.cor.X##rcv#glmnet
## feats max.nTuningRuns
## 1 sqft_living,bedrooms,floors,sqft_lot,.rnorm,zipcode 25
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 4.267 0.011 0.5235209
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 261380.6 0.5233606 0.4523824 228629.4 0.4515097
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5232467 7008.696 0.003291568fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 4 fit.models_0_Low.cor.X 1 3 glmnet 58.503 64.362
## 5 fit.models_0_end 1 4 teardown 64.362 NA
## elapsed
## 4 5.859
## 5 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 15 fit.models 8 0 0 44.348 64.372 20.024
## 16 fit.models 8 1 1 64.373 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 65.456 NA NA#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
if (length(interact_vars) > 0) {
indep_vars <-
setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indep_vars <- union(indep_vars, topindep_var)
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
if ((length(indep_vars) == 1) && (grepl("^%<d-%", indep_vars))) {
indep_vars <-
eval(parse(text = str_trim(unlist(strsplit(indep_vars, "%<d-%"))[2])))
}
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indep_vars]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indep_vars]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 65.456 65.466
## 2 fit.models_1_All.X 1 1 setup 65.467 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 65.467 65.473
## 3 fit.models_1_All.X 1 2 glmnet 65.474 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indep_vars: sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 249 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 70 -none- numeric
## beta 490 dgCMatrix S4
## df 70 -none- numeric
## dim 2 -none- numeric
## lambda 70 -none- numeric
## dev.ratio 70 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 7 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .rnorm bathrooms bedrooms floors
## -5.622646e+07 -6.671834e+02 1.011514e+04 -5.647029e+04 -5.689017e+03
## sqft_living sqft_lot zipcode
## 3.227395e+02 -3.060810e-01 5.738244e+02
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".rnorm" "bathrooms" "bedrooms" "floors"
## [6] "sqft_living" "sqft_lot" "zipcode"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.937 0.011
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5237697 261392.5 0.5235827 0.4525405 228596.4
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4515223 0.5231978 7041.37 0.003079023
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 65.474 71.092
## 4 fit.models_1_All.X 1 3 glm 71.093 NA
## elapsed
## 3 5.618
## 4 NA
## [1] "fitting model: All.X##rcv#glm"
## [1] " indep_vars: sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1716681 -143674 -22405 101857 4059905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.722e+07 3.694e+06 -15.489 < 2e-16 ***
## .rnorm -1.077e+03 1.969e+03 -0.547 0.5845
## bathrooms 1.090e+04 4.271e+03 2.552 0.0107 *
## bedrooms -5.812e+04 2.600e+03 -22.351 < 2e-16 ***
## floors -7.724e+03 4.244e+03 -1.820 0.0688 .
## sqft_living 3.246e+02 3.461e+00 93.810 < 2e-16 ***
## sqft_lot -3.227e-01 4.767e-02 -6.770 1.33e-11 ***
## zipcode 5.840e+02 3.765e+01 15.510 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 68184818437)
##
## Null deviance: 2.5534e+15 on 17840 degrees of freedom
## Residual deviance: 1.2159e+15 on 17833 degrees of freedom
## AIC: 495693
##
## Number of Fisher Scoring iterations: 2
##
## id
## 1 All.X##rcv#glm
## feats
## 1 sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.687 0.057
## max.R.sq.fit min.RMSE.fit min.aic.fit max.Adj.R.sq.fit max.R.sq.OOB
## 1 0.5237949 261401.4 495693 0.523608 0.4517648
## min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit
## 1 228758.2 0.4507453 0.5231748 6983.335
## max.RsquaredSD.fit
## 1 0.003064746# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 71.093 76.7
## 5 fit.models_1_preProc 1 4 preProc 76.701 NA
## elapsed
## 4 5.607
## 5 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## Max.cor.Y.rcv.1X1###glmnet sqft_living,bedrooms
## Max.cor.Y##rcv#rpart sqft_living,bedrooms
## Interact.High.cor.Y##rcv#glmnet sqft_living,bedrooms,sqft_living:sqft_living
## Low.cor.X##rcv#glmnet sqft_living,bedrooms,floors,sqft_lot,.rnorm,zipcode
## All.X##rcv#glmnet sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode
## All.X##rcv#glm sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode
## max.nTuningRuns min.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 0 0.666
## Max.cor.Y##rcv#rpart 5 3.112
## Interact.High.cor.Y##rcv#glmnet 25 5.070
## Low.cor.X##rcv#glmnet 25 4.267
## All.X##rcv#glmnet 25 3.937
## All.X##rcv#glm 1 1.687
## min.elapsedtime.final max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0.013 0.5151398
## Max.cor.Y##rcv#rpart 0.076 0.4978572
## Interact.High.cor.Y##rcv#glmnet 0.010 0.5153775
## Low.cor.X##rcv#glmnet 0.011 0.5235209
## All.X##rcv#glmnet 0.011 0.5237697
## All.X##rcv#glm 0.057 0.5237949
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 263425.4 0.5150854 0.4469051
## Max.cor.Y##rcv#rpart 271576.7 NA 0.3939231
## Interact.High.cor.Y##rcv#glmnet 263555.0 0.5153231 0.4451580
## Low.cor.X##rcv#glmnet 261380.6 0.5233606 0.4523824
## All.X##rcv#glmnet 261392.5 0.5235827 0.4525405
## All.X##rcv#glm 261401.4 0.5236080 0.4517648
## min.RMSE.OOB max.Adj.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 229769.9 0.4466116
## Max.cor.Y##rcv#rpart 240523.3 NA
## Interact.High.cor.Y##rcv#glmnet 230132.5 0.4448636
## Low.cor.X##rcv#glmnet 228629.4 0.4515097
## All.X##rcv#glmnet 228596.4 0.4515223
## All.X##rcv#glm 228758.2 0.4507453
## max.Rsquared.fit min.RMSESD.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.4858920 4805.365
## Interact.High.cor.Y##rcv#glmnet 0.5152638 6967.464
## Low.cor.X##rcv#glmnet 0.5232467 7008.696
## All.X##rcv#glmnet 0.5231978 7041.370
## All.X##rcv#glm 0.5231748 6983.335
## max.RsquaredSD.fit min.aic.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.012588873 NA
## Interact.High.cor.Y##rcv#glmnet 0.003882658 NA
## Low.cor.X##rcv#glmnet 0.003291568 NA
## All.X##rcv#glmnet 0.003079023 NA
## All.X##rcv#glm 0.003064746 495693rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 76.701 76.813
## 6 fit.models_1_end 1 5 teardown 76.814 NA
## elapsed
## 5 0.112
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 1 1 64.373 76.825 12.453
## 17 fit.models 8 2 2 76.826 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 80.279 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## Max.cor.Y.rcv.1X1###glmnet sqft_living,bedrooms
## Max.cor.Y##rcv#rpart sqft_living,bedrooms
## Interact.High.cor.Y##rcv#glmnet sqft_living,bedrooms,sqft_living:sqft_living
## Low.cor.X##rcv#glmnet sqft_living,bedrooms,floors,sqft_lot,.rnorm,zipcode
## All.X##rcv#glmnet sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode
## All.X##rcv#glm sqft_living,bathrooms,bedrooms,floors,sqft_lot,.rnorm,zipcode
## max.nTuningRuns max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0 0.5151398
## Max.cor.Y##rcv#rpart 5 0.4978572
## Interact.High.cor.Y##rcv#glmnet 25 0.5153775
## Low.cor.X##rcv#glmnet 25 0.5235209
## All.X##rcv#glmnet 25 0.5237697
## All.X##rcv#glm 1 0.5237949
## max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 0.5150854 0.4469051
## Max.cor.Y##rcv#rpart NA 0.3939231
## Interact.High.cor.Y##rcv#glmnet 0.5153231 0.4451580
## Low.cor.X##rcv#glmnet 0.5233606 0.4523824
## All.X##rcv#glmnet 0.5235827 0.4525405
## All.X##rcv#glm 0.5236080 0.4517648
## max.Adj.R.sq.OOB max.Rsquared.fit
## Max.cor.Y.rcv.1X1###glmnet 0.4466116 NA
## Max.cor.Y##rcv#rpart NA 0.4858920
## Interact.High.cor.Y##rcv#glmnet 0.4448636 0.5152638
## Low.cor.X##rcv#glmnet 0.4515097 0.5232467
## All.X##rcv#glmnet 0.4515223 0.5231978
## All.X##rcv#glm 0.4507453 0.5231748
## inv.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 1.5015015
## Max.cor.Y##rcv#rpart 0.3213368
## Interact.High.cor.Y##rcv#glmnet 0.1972387
## Low.cor.X##rcv#glmnet 0.2343567
## All.X##rcv#glmnet 0.2540005
## All.X##rcv#glm 0.5927682
## inv.elapsedtime.final inv.RMSE.fit
## Max.cor.Y.rcv.1X1###glmnet 76.92308 3.796141e-06
## Max.cor.Y##rcv#rpart 13.15789 3.682201e-06
## Interact.High.cor.Y##rcv#glmnet 100.00000 3.794274e-06
## Low.cor.X##rcv#glmnet 90.90909 3.825839e-06
## All.X##rcv#glmnet 90.90909 3.825665e-06
## All.X##rcv#glm 17.54386 3.825534e-06
## inv.RMSE.OOB inv.aic.fit
## Max.cor.Y.rcv.1X1###glmnet 4.352180e-06 NA
## Max.cor.Y##rcv#rpart 4.157601e-06 NA
## Interact.High.cor.Y##rcv#glmnet 4.345323e-06 NA
## Low.cor.X##rcv#glmnet 4.373891e-06 NA
## All.X##rcv#glmnet 4.374523e-06 NA
## All.X##rcv#glm 4.371427e-06 2.017378e-06# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id min.RMSE.OOB max.R.sq.OOB
## 5 All.X##rcv#glmnet 228596.4 0.4525405
## 4 Low.cor.X##rcv#glmnet 228629.4 0.4523824
## 6 All.X##rcv#glm 228758.2 0.4517648
## 1 Max.cor.Y.rcv.1X1###glmnet 229769.9 0.4469051
## 3 Interact.High.cor.Y##rcv#glmnet 230132.5 0.4451580
## 2 Max.cor.Y##rcv#rpart 240523.3 0.3939231
## max.Adj.R.sq.fit min.RMSE.fit
## 5 0.5235827 261392.5
## 4 0.5233606 261380.6
## 6 0.5236080 261401.4
## 1 0.5150854 263425.4
## 3 0.5153231 263555.0
## 2 NA 271576.7# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fit + min.RMSE.fit
## <environment: 0x7f90a4f54c70>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: All.X##rcv#glmnet"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indep_vars <- intersect(indep_vars, names(glbObsFit))
# indep_vars <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indep_vars)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 70 -none- numeric
## beta 490 dgCMatrix S4
## df 70 -none- numeric
## dim 2 -none- numeric
## lambda 70 -none- numeric
## dev.ratio 70 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 7 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .rnorm bathrooms bedrooms floors
## -5.622646e+07 -6.671834e+02 1.011514e+04 -5.647029e+04 -5.689017e+03
## sqft_living sqft_lot zipcode
## 3.227395e+02 -3.060810e-01 5.738244e+02
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".rnorm" "bathrooms" "bedrooms" "floors"
## [6] "sqft_living" "sqft_lot" "zipcode"## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp All.X##rcv#glmnet.imp
## bathrooms 100.00000 100.00000 100.00000
## zipcode 85.67056 85.67056 85.67056
## sqft_living 85.29348 85.29348 85.29348
## sqft_lot 84.80832 84.80832 84.80832
## .rnorm 83.80678 83.80678 83.80678
## floors 76.26484 76.26484 76.26484
## bedrooms 0.00000 0.00000 0.00000#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 7
## id date price bedrooms bathrooms sqft_living
## 18200 3625059152 20141230T000000 3300000 3 3.25 4220
## 11976 1118000340 20150408T000000 3000000 5 3.75 4590
## 16970 3025059093 20140729T000000 3100000 5 5.25 5090
## 7433 5316100780 20140922T000000 2575000 4 3.50 3280
## 17152 4107100190 20150324T000000 2500000 4 3.75 3480
## sqft_lot floors waterfront view condition grade sqft_above
## 18200 41300 1 1 4 4 11 2460
## 11976 11265 2 0 0 4 11 3450
## 16970 23669 2 0 0 3 12 5090
## 7433 3800 2 0 0 3 11 2880
## 17152 14850 1 0 4 3 9 1870
## sqft_basement yr_built yr_renovated zipcode lat long
## 18200 1760 1958 1987 98008 47.6083 -122.110
## 11976 1140 1927 0 98112 47.6389 -122.288
## 16970 0 2006 0 98004 47.6297 -122.216
## 7433 400 2011 0 98112 47.6299 -122.280
## 17152 1610 1951 2013 98004 47.6227 -122.216
## sqft_living15 sqft_lot15 .src .rnorm .pos
## 18200 3810 30401 Test 1.5699436 21062
## 11976 3870 8996 Test -0.4948976 19932
## 16970 3830 22605 Test -1.3551279 20831
## 7433 2050 3800 Test 0.3383154 19156
## 17152 4780 18480 Test 0.2021981 20860
## id.date sqft.living.cut.fctr
## 18200 3625059152#20141230T000000 (2.55e+03,1.4e+04]
## 11976 1118000340#20150408T000000 (2.55e+03,1.4e+04]
## 16970 3025059093#20140729T000000 (2.55e+03,1.4e+04]
## 7433 5316100780#20140922T000000 (2.55e+03,1.4e+04]
## 17152 4107100190#20150324T000000 (2.55e+03,1.4e+04]
## price.All.X..rcv.glmnet price.All.X..rcv.glmnet.err
## 18200 1218963.9 2081036
## 11976 1295054.0 1704946
## 16970 1406400.7 1693599
## 7433 927935.7 1647064
## 17152 935437.0 1564563
## price.All.X..rcv.glmnet.err.abs price.All.X..rcv.glmnet.is.acc
## 18200 2081036 FALSE
## 11976 1704946 FALSE
## 16970 1693599 FALSE
## 7433 1647064 FALSE
## 17152 1564563 FALSE
## .label
## 18200 3625059152#20141230T000000
## 11976 1118000340#20150408T000000
## 16970 3025059093#20140729T000000
## 7433 5316100780#20140922T000000
## 17152 4107100190#20150324T000000
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## sqft.living.cut.fctr .n.OOB .n.Fit .n.Tst
## (2.55e+03,1.4e+04] (2.55e+03,1.4e+04] 872 4487 872
## (1.91e+03,2.55e+03] (1.91e+03,2.55e+03] 963 4471 963
## (1.43e+03,1.91e+03] (1.43e+03,1.91e+03] 988 4428 988
## (0,1.43e+03] (0,1.43e+03] 949 4455 949
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## (2.55e+03,1.4e+04] 0.2514994 0.2311771 0.2311771
## (1.91e+03,2.55e+03] 0.2506025 0.2553022 0.2553022
## (1.43e+03,1.91e+03] 0.2481924 0.2619300 0.2619300
## (0,1.43e+03] 0.2497057 0.2515907 0.2515907
## err.abs.fit.sum err.abs.fit.mean .n.fit
## (2.55e+03,1.4e+04] 1293759192 288335.0 4487
## (1.91e+03,2.55e+03] 731606056 163633.7 4471
## (1.43e+03,1.91e+03] 554699721 125270.9 4428
## (0,1.43e+03] 471510132 105838.4 4455
## err.abs.OOB.sum err.abs.OOB.mean
## (2.55e+03,1.4e+04] 230493369 264327.3
## (1.91e+03,2.55e+03] 152287548 158138.7
## (1.43e+03,1.91e+03] 120331898 121793.4
## (0,1.43e+03] 101432524 106883.6
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 3772.0 17841.0 3772.0 1.0
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.0 1.0 3051575100.0 683078.0
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 17841.0 604545339.3 651142.9write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 91.764 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 2 2 76.826 91.788 14.963
## 18 fit.models 8 3 3 91.789 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glbObsAll, #glbObsTrn, glbObsFit, glbObsOOB, glbObsNew,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 18 fit.models 8 3 3 91.789 100.229
## 19 fit.data.training 9 0 0 100.229 NA
## elapsed
## 18 8.44
## 19 NAStep 9.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))[, "id"])
rfe_trn_results <-
myrun_rfe(glbObsTrn, indep_vars, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indep_vars = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
indep_vars_vctr <- myextract_actual_feats(predictors(rfe_trn_results))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final")
trnobs_df <- if (is.null(glbObsTrnOutliers[[mdl_id_pfx]])) glbObsTrn else
glbObsTrn[!(glbObsTrn[, glbFeatsId] %in%
glbObsTrnOutliers[[mdl_id_pfx]]), ]
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
}
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}## Warning: Final model same as glb_sel_mdl_idrm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 19 fit.data.training 9 0 0 100.229 100.682
## 20 fit.data.training 9 1 1 100.682 NA
## elapsed
## 19 0.453
## 20 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp.x All.X##rcv#glmnet.imp
## bathrooms 100.00000 100.00000
## zipcode 85.67056 85.67056
## sqft_living 85.29348 85.29348
## sqft_lot 84.80832 84.80832
## .rnorm 83.80678 83.80678
## floors 76.26484 76.26484
## bedrooms 0.00000 0.00000
## All.X..rcv.glmnet.imp.y imp Final.All.X##rcv#glmnet.imp
## bathrooms 100.00000 100.00000 100.00000
## zipcode 85.67056 85.67056 85.67056
## sqft_living 85.29348 85.29348 85.29348
## sqft_lot 84.80832 84.80832 84.80832
## .rnorm 83.80678 83.80678 83.80678
## floors 76.26484 76.26484 76.26484
## bedrooms 0.00000 0.00000 0.00000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 7
## id date price bedrooms bathrooms sqft_living
## 3915 9808700762 20140611T000000 7062500 5 4.50 10040
## 7253 6762700020 20141013T000000 7700000 6 8.00 12050
## 9255 9208900037 20140919T000000 6885000 6 7.75 9890
## 1316 7558700030 20150413T000000 5300000 6 6.00 7390
## 1449 8907500070 20150413T000000 5350000 5 5.00 8000
## sqft_lot floors condition grade sqft_above yr_built zipcode lat
## 3915 37325 2.0 3 11 7680 1940 98004 47.6500
## 7253 27600 2.5 4 13 8570 1910 98102 47.6298
## 9255 31374 2.0 3 13 8860 2001 98039 47.6305
## 1316 24829 2.0 4 12 5000 1991 98040 47.5631
## 1449 23985 2.0 3 12 6720 2009 98004 47.6232
## long sqft_living15 sqft_lot15 .src .rnorm .pos
## 3915 -122.214 3930 25449 Train 0.7554864 3215
## 7253 -122.323 3940 8800 Train 0.4283131 5972
## 9255 -122.240 4540 42730 Train 0.6897146 7619
## 1316 -122.210 4320 24619 Train 1.8434675 1073
## 1449 -122.220 4600 21750 Train 0.1298099 1180
## id.date sqft.living.cut.fctr .lcn waterfront view
## 3915 9808700762#20140611T000000 (2.55e+03,1.4e+04] Fit 1 2
## 7253 6762700020#20141013T000000 (2.55e+03,1.4e+04] Fit 0 3
## 9255 9208900037#20140919T000000 (2.55e+03,1.4e+04] Fit 0 4
## 1316 7558700030#20150413T000000 (2.55e+03,1.4e+04] Fit 1 4
## 1449 8907500070#20150413T000000 (2.55e+03,1.4e+04] Fit 0 4
## sqft_basement yr_renovated price.All.X..rcv.glmnet
## 3915 2360 2001 2990787
## 7253 3480 1987 3675012
## 9255 1030 0 2940729
## 1316 2390 0 2117986
## 1449 1280 0 2341957
## price.All.X..rcv.glmnet.err price.All.X..rcv.glmnet.err.abs
## 3915 -4071713 4071713
## 7253 -4024988 4024988
## 9255 -3944271 3944271
## 1316 -3182014 3182014
## 1449 -3008043 3008043
## price.All.X..rcv.glmnet.is.acc price.Final.All.X..rcv.glmnet
## 3915 FALSE 2990787
## 7253 FALSE 3675012
## 9255 FALSE 2940729
## 1316 FALSE 2117986
## 1449 FALSE 2341957
## price.Final.All.X..rcv.glmnet.err
## 3915 4071713
## 7253 4024988
## 9255 3944271
## 1316 3182014
## 1449 3008043
## price.Final.All.X..rcv.glmnet.err.abs
## 3915 4071713
## 7253 4024988
## 9255 3944271
## 1316 3182014
## 1449 3008043
## price.Final.All.X..rcv.glmnet.is.acc .label
## 3915 FALSE 9808700762#20140611T000000
## 7253 FALSE 6762700020#20141013T000000
## 9255 FALSE 9208900037#20140919T000000
## 1316 FALSE 7558700030#20150413T000000
## 1449 FALSE 8907500070#20150413T000000
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "price.Final.All.X..rcv.glmnet"
## [2] "price.Final.All.X..rcv.glmnet.err"
## [3] "price.Final.All.X..rcv.glmnet.err.abs"
## [4] "price.Final.All.X..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)if (glb_save_envir)
save(glb_feats_df, glbObsAll,
#glbObsTrn, glbObsFit, glbObsOOB, glbObsNew,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file = paste0(glb_out_pfx, "dsk.RData"))
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glb_out_pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 1 1 100.682 130.134
## 21 predict.data.new 10 0 0 130.135 NA
## elapsed
## 20 29.453
## 21 NAStep 10.0: predict data new
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 7
## id date price bedrooms bathrooms sqft_living
## 18200 3625059152 20141230T000000 3300000 3 3.25 4220
## 11976 1118000340 20150408T000000 3000000 5 3.75 4590
## 16970 3025059093 20140729T000000 3100000 5 5.25 5090
## 7433 5316100780 20140922T000000 2575000 4 3.50 3280
## 17152 4107100190 20150324T000000 2500000 4 3.75 3480
## sqft_lot floors condition grade sqft_above yr_built zipcode lat
## 18200 41300 1 4 11 2460 1958 98008 47.6083
## 11976 11265 2 4 11 3450 1927 98112 47.6389
## 16970 23669 2 3 12 5090 2006 98004 47.6297
## 7433 3800 2 3 11 2880 2011 98112 47.6299
## 17152 14850 1 3 9 1870 1951 98004 47.6227
## long sqft_living15 sqft_lot15 .src .rnorm .pos
## 18200 -122.110 3810 30401 Test 1.5699436 21062
## 11976 -122.288 3870 8996 Test -0.4948976 19932
## 16970 -122.216 3830 22605 Test -1.3551279 20831
## 7433 -122.280 2050 3800 Test 0.3383154 19156
## 17152 -122.216 4780 18480 Test 0.2021981 20860
## id.date sqft.living.cut.fctr .lcn
## 18200 3625059152#20141230T000000 (2.55e+03,1.4e+04] OOB
## 11976 1118000340#20150408T000000 (2.55e+03,1.4e+04] OOB
## 16970 3025059093#20140729T000000 (2.55e+03,1.4e+04] OOB
## 7433 5316100780#20140922T000000 (2.55e+03,1.4e+04] OOB
## 17152 4107100190#20150324T000000 (2.55e+03,1.4e+04] OOB
## price.Final.All.X..rcv.glmnet price.Final.All.X..rcv.glmnet.err
## 18200 1218963.9 2081036
## 11976 1295054.0 1704946
## 16970 1406400.7 1693599
## 7433 927935.7 1647064
## 17152 935437.0 1564563
## price.Final.All.X..rcv.glmnet.err.abs
## 18200 2081036
## 11976 1704946
## 16970 1693599
## 7433 1647064
## 17152 1564563
## price.Final.All.X..rcv.glmnet.is.acc .label
## 18200 FALSE 3625059152#20141230T000000
## 11976 FALSE 1118000340#20150408T000000
## 16970 FALSE 3025059093#20140729T000000
## 7433 FALSE 5316100780#20140922T000000
## 17152 FALSE 4107100190#20150324T000000
## Loading required package: stringr## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"## [1] "glb_fin_mdl_id: Final.All.X##rcv#glmnet"## [1] "Cross Validation issues:"
## Max.cor.Y.rcv.1X1###glmnet
## 0## min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## All.X##rcv#glmnet 228596.4 0.4525405 0.5235827
## Low.cor.X##rcv#glmnet 228629.4 0.4523824 0.5233606
## All.X##rcv#glm 228758.2 0.4517648 0.5236080
## Max.cor.Y.rcv.1X1###glmnet 229769.9 0.4469051 0.5150854
## Interact.High.cor.Y##rcv#glmnet 230132.5 0.4451580 0.5153231
## Max.cor.Y##rcv#rpart 240523.3 0.3939231 NA
## min.RMSE.fit
## All.X##rcv#glmnet 261392.5
## Low.cor.X##rcv#glmnet 261380.6
## All.X##rcv#glm 261401.4
## Max.cor.Y.rcv.1X1###glmnet 263425.4
## Interact.High.cor.Y##rcv#glmnet 263555.0
## Max.cor.Y##rcv#rpart 271576.7## [1] "All.X##rcv#glmnet OOB RMSE: 228596.3688"## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## (2.55e+03,1.4e+04] 0.2514994 0.2311771 0.2311771 4487
## (1.91e+03,2.55e+03] 0.2506025 0.2553022 0.2553022 4471
## (1.43e+03,1.91e+03] 0.2481924 0.2619300 0.2619300 4428
## (0,1.43e+03] 0.2497057 0.2515907 0.2515907 4455
## .n.OOB .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## (2.55e+03,1.4e+04] 872 872 4487 872 4487 264327.3
## (1.91e+03,2.55e+03] 963 963 4471 963 4471 158138.7
## (1.43e+03,1.91e+03] 988 988 4428 988 4428 121793.4
## (0,1.43e+03] 949 949 4455 949 4455 106883.6
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## (2.55e+03,1.4e+04] 288335.0 264327.3 288335.0
## (1.91e+03,2.55e+03] 163633.7 158138.7 163633.7
## (1.43e+03,1.91e+03] 125270.9 121793.4 125270.9
## (0,1.43e+03] 105838.4 106883.6 105838.4
## err.abs.OOB.sum err.abs.fit.sum err.abs.new.sum
## (2.55e+03,1.4e+04] 230493369 1293759192 230493369
## (1.91e+03,2.55e+03] 152287548 731606056 152287548
## (1.43e+03,1.91e+03] 120331898 554699721 120331898
## (0,1.43e+03] 101432524 471510132 101432524
## err.abs.trn.sum
## (2.55e+03,1.4e+04] 1293759192
## (1.91e+03,2.55e+03] 731606056
## (1.43e+03,1.91e+03] 554699721
## (0,1.43e+03] 471510132
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.0 1.0 1.0 17841.0
## .n.OOB .n.Tst .n.fit .n.new
## 3772.0 3772.0 17841.0 3772.0
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 17841.0 651142.9 683078.0 651142.9
## err.abs.trn.mean err.abs.OOB.sum err.abs.fit.sum err.abs.new.sum
## 683078.0 604545339.3 3051575100.0 604545339.3
## err.abs.trn.sum
## 3051575100.0
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## (2.55e+03,1.4e+04] 0.2514994 0.2311771 0.2311771 4487
## (1.91e+03,2.55e+03] 0.2506025 0.2553022 0.2553022 4471
## (1.43e+03,1.91e+03] 0.2481924 0.2619300 0.2619300 4428
## (0,1.43e+03] 0.2497057 0.2515907 0.2515907 4455
## .n.OOB .n.Tst .n.fit .n.new.x .n.new.y .n.trn
## (2.55e+03,1.4e+04] 872 872 4487 872 872 4487
## (1.91e+03,2.55e+03] 963 963 4471 963 963 4471
## (1.43e+03,1.91e+03] 988 988 4428 988 988 4428
## (0,1.43e+03] 949 949 4455 949 949 4455
## err.abs.OOB.mean err.abs.fit.mean err.abs.trn.mean
## (2.55e+03,1.4e+04] 264327.3 288335.0 288335.0
## (1.91e+03,2.55e+03] 158138.7 163633.7 163633.7
## (1.43e+03,1.91e+03] 121793.4 125270.9 125270.9
## (0,1.43e+03] 106883.6 105838.4 105838.4
## err.abs.new.mean.x err.abs.new.mean.y err.abs.OOB.sum
## (2.55e+03,1.4e+04] 264327.3 264327.3 230493369
## (1.91e+03,2.55e+03] 158138.7 158138.7 152287548
## (1.43e+03,1.91e+03] 121793.4 121793.4 120331898
## (0,1.43e+03] 106883.6 106883.6 101432524
## err.abs.fit.sum err.abs.trn.sum err.abs.new.sum.x
## (2.55e+03,1.4e+04] 1293759192 1293759192 230493369
## (1.91e+03,2.55e+03] 731606056 731606056 152287548
## (1.43e+03,1.91e+03] 554699721 554699721 120331898
## (0,1.43e+03] 471510132 471510132 101432524
## err.abs.new.sum.y
## (2.55e+03,1.4e+04] 230493369
## (1.91e+03,2.55e+03] 152287548
## (1.43e+03,1.91e+03] 120331898
## (0,1.43e+03] 101432524
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## 1.0 1.0 1.0
## .n.Fit .n.OOB .n.Tst
## 17841.0 3772.0 3772.0
## .n.fit .n.new.x .n.new.y
## 17841.0 3772.0 3772.0
## .n.trn err.abs.OOB.mean err.abs.fit.mean
## 17841.0 651142.9 683078.0
## err.abs.trn.mean err.abs.new.mean.x err.abs.new.mean.y
## 683078.0 651142.9 651142.9
## err.abs.OOB.sum err.abs.fit.sum err.abs.trn.sum
## 604545339.3 3051575100.0 3051575100.0
## err.abs.new.sum.x err.abs.new.sum.y
## 604545339.3 604545339.3## [1] "Final.All.X##rcv#glmnet prediction stats for glbObsNew:"
## id max.R.sq.new min.RMSE.new max.Adj.R.sq.new
## 1 All.X##rcv#glmnet 0.4525405 228596.4 0.4515223## All.X..rcv.glmnet.imp.x All.X__rcv_glmnet.imp
## bathrooms 100.00000 100.00000
## zipcode 85.67056 85.67056
## sqft_living 85.29348 85.29348
## sqft_lot 84.80832 84.80832
## .rnorm 83.80678 83.80678
## floors 76.26484 76.26484
## All.X..rcv.glmnet.imp.y Final.All.X__rcv_glmnet.imp
## bathrooms 100.00000 100.00000
## zipcode 85.67056 85.67056
## sqft_living 85.29348 85.29348
## sqft_lot 84.80832 84.80832
## .rnorm 83.80678 83.80678
## floors 76.26484 76.26484## [1] "glbObsNew prediction stats:"## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## label step_major step_minor label_minor bgn end
## 21 predict.data.new 10 0 0 130.135 167.872
## 22 display.session.info 11 0 0 167.872 NA
## elapsed
## 21 37.737
## 22 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 21 predict.data.new 10 0 0 130.135
## 20 fit.data.training 9 1 1 100.682
## 15 fit.models 8 0 0 44.348
## 2 inspect.data 2 0 0 16.182
## 17 fit.models 8 2 2 76.826
## 16 fit.models 8 1 1 64.373
## 18 fit.models 8 3 3 91.789
## 1 import.data 1 0 0 8.722
## 3 scrub.data 2 1 1 32.091
## 14 select.features 7 0 0 40.360
## 10 extract.features.end 3 5 5 38.552
## 13 partition.data.training 6 0 0 39.803
## 19 fit.data.training 9 0 0 100.229
## 11 manage.missing.data 4 0 0 39.429
## 12 cluster.data 5 0 0 39.741
## 6 extract.features.string 3 1 1 38.373
## 9 extract.features.text 3 4 4 38.498
## 4 transform.data 2 2 2 38.302
## 7 extract.features.datetime 3 2 2 38.427
## 8 extract.features.price 3 3 3 38.463
## 5 extract.features 3 0 0 38.343
## end elapsed duration
## 21 167.872 37.737 37.737
## 20 130.134 29.453 29.452
## 15 64.372 20.024 20.024
## 2 32.090 15.908 15.908
## 17 91.788 14.963 14.962
## 16 76.825 12.453 12.452
## 18 100.229 8.440 8.440
## 1 16.182 7.460 7.460
## 3 38.302 6.211 6.211
## 14 44.347 3.988 3.987
## 10 39.428 0.876 0.876
## 13 40.360 0.557 0.557
## 19 100.682 0.453 0.453
## 11 39.740 0.312 0.311
## 12 39.802 0.061 0.061
## 6 38.427 0.054 0.054
## 9 38.551 0.053 0.053
## 4 38.342 0.040 0.040
## 7 38.463 0.036 0.036
## 8 38.498 0.035 0.035
## 5 38.372 0.029 0.029
## [1] "Total Elapsed Time: 167.872 secs"